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Comprehensive Review and Analysis on Facial Emotion Recognition:Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

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Computers, Materials & Continua 2025年第1期82卷 41-72页

作者： Amjad Rehman Muhammad Mujahid Alex Elyassih Bayan AlGhofaily Saeed Ali Omer Bahaj Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia MIS Department College of Business Administration Prince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia

In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry *** demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial *** study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each *** immense achievement of deep learning has resulted in a growing use of its much architecture to enhance *** review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of *** following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics *** this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further *** information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.

关键词： Face emotion recognition deep learning hybrid learning CK+ facial images machine learning technological development

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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C.A.L.I.X: Leveraging Hybrid RAG for Responsible AI in the Medical Field

C.A.L.I.X: Leveraging Hybrid RAG for Responsible AI in the M...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Muddassir Khalidi Arwa Bawazir Tanzila Saba Zainab Mariya Mohiuddin Saeed lababidi Anees Ara Asma Vaheed Khan Abdul Rahman Mamdouh Artificial Intelligence and Data Analytics Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Conversational AI memory systems work much like human memory. They record, maintain, and retrieve contextual information from earlier interactions, just as human memory draws on past experiences and learned knowledge to shape understanding and responses. However, individuals often face challenges with long-term recall of specific details from past events and short-term working memory, such as retaining lists, which can limit their ability to provide well-informed and nuanced responses. This research aims at implementing a simple Retrieve-Augment-Generate (RAG) system to implement a memory augmentation system with a particular focus on a use case in the medical field. This paper introduces C.A.L.I.X (Cognitive Archive for Learning and Information Exchange), an AI powered conversational memory assistant designed in alignment with Responsible AI principles. CA.L.I.X has been engineered to monitor conversations and extract data using voice interactions, ensuring that the information gathered is reliable. Future efforts will be focused on enhancing the accuracy of memory retrieval, streamlining the control of memory decay, and elevating the overall user experience.

关键词： Conversational artificial intelligence Accuracy Memory management Medical services Documentation Vectors User experience Time factors Information exchange Strain

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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Computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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Towards Ergonomic Typing: Real-Time Typing Posture Classification on WatchOS

Towards Ergonomic Typing: Real-Time Typing Posture Classific...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fetoun AlZahrani Arwa Ghouti Tanzila Saba Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

With the increasing prevalence of computer usage, there has been a rise in cases of repetitive strain injuries (RSIs) like Carpal Tunnel Syndrome (CTS) among individuals who spend extended hours typing. Hand pose estimation plays a vital role in improving ergonomic and safe human-computer interaction (HCI). To address this challenge, we propose ErgoType, a real-time hand pose estimation application developed for Apple WatchOS. The application delivers posture classification results directly on the Apple Watch and integrates with a companion iPhone application for detailed feedback. The system employs a Support Vector Machine (SVM) model trained on smartwatch emulator data to analyze key hand-position indicators, such as wrist and elbow angles, achieving a classification accuracy of 98%. Immediate feedback is provided via on-screen notifications, alerting users to poor typing habits such as excessive wrist flexion or prolonged typing without rest. This feedback encourages users to adopt healthier typing practices. ErgoType's combination of wearable technology and real-time feedback offers a proactive solution for reducing typing-related injuries and promoting workplace wellness. It is designed to support both professionals and frequent computer users, enhancing productivity and well-being through improved ergonomics.

关键词： Support vector machines Wrist Hands Carpal tunnel syndrome Wearable Health Monitoring Systems Ergonomics Pose estimation Real-time systems Injuries Strain

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data Frequency Coverage Impact on AI Performance 18

Data Frequency Coverage Impact on AI Performance

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Lanus, Erin Lee, Brian Chandrasekaran, Jaganmohan Freeman, Laura J. Raunak, M.S. Kacker, Raghu N. Richard Kuhn, D. Virginia Tech National Security Institute ArlingtonVA United States Virginia Tech Sanghani Center for Artificial Intelligence & Data Analytics ArlingtonVA United States National Institute of Standards and Technology Information Technology Lab MD United States

ISBN: (纸本)9798331534677

artificial intelligence (AI) models use statistical learning over data to solve complex problems for which straightforward rules or algorithms may be difficult or impossible to design;however, a side effect is that models that are complex enough to sufficiently represent the function may be uninterpretable. Combinatorial testing, a black-box approach arising from software testing, has been applied to test AI models. A key differentiator between traditional software and AI is that many traditional software faults are deterministic, requiring a failureinducing combination of inputs to appear only once in the test set for it to be discovered. On the other hand, AI models learn statistically by reinforcing weights through repeated appearances in the training dataset, and the frequency of input combinations plays a significant role in influencing the model's behavior. Thus, a single occurrence of a combination of feature values may not be sufficient to influence the model's behavior. Consequently, measures like simple combinatorial coverage that are applicable to software testing do not capture the frequency with which interactions are covered in the AI model's input space. This work develops methods to characterize the data frequency coverage of feature interactions in training datasets and analyze the impact of imbalance, or skew, in the combinatorial frequency coverage of the training data on model performance. We demonstrate our methods with experiments on an open-source dataset using several classical machine learning algorithms. This pilot study makes three observations: performance may increase or decrease with data skew, feature importance methods do not predict skew impact, and adding more data may not mitigate skew effects. © 2025 IEEE.

关键词： Software testing

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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Computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Noise filtering approach to improve handwritten digit recognition using customized CNN for Cerebral Palsy individuals

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The European Physical Journal Special Topics 2025年 1-19页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology-Tiruchirappalli Tiruchirappalli India Centre of Excellence (CoE) in Artificial Intelligence National Institute of Technology-Tiruchirappalli Tiruchirappalli India Information Processing Lab Department of Computer Applications Tiruchirappalli India

Automatic recognition of handwritten digits in people with Cerebral Palsy (CP) is a serious challenge that requires advancements in data preparation for improved predictive accuracy. The disorganized digit pixel patterns caused by CP people’ innate motor coordination problems result in the classification errors due to the noise and outlier. Despite the fact that many handwritten digit databases for normal people are available, none expressly cater to those with shaky hands/CP dataset. This highlights the importance of an efficient pre-processing method for mitigating variances in CP individuals’ digit pixel patterns. A total of 43,000 images from 43 CP people were collected and organized into 10 different folders for data labeling. The authors used the K-Means Based Noise Removal (KMNR) approach to remove noise and outliers from a CP and tested it on a customized CNN. The KMNR model achieved an accuracy of 98.86%, compared to 93.54% without it. The F1-score values for state-of-the-art models without KMNR ranged from 85.00% to 97.00%, but after integrating KMNR, they increased to 92.00% to 98.00%. The precision values ranged from 85.00% to 97.00% in the absence of KMNR, but increased to 91.00% to 98.00% in the presence of KMNR. The recall values ranged from 85.00% to 97.00% without KMNR, but increased to 87.00% to 98.00% with KMNR.

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data Frequency Coverage Impact on AI Performance

Data Frequency Coverage Impact on AI Performance

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Erin Lanus Brian Lee Jaganmohan Chandrasekaran Laura J. Freeman M S Raunak Raghu N. Kacker D. Richard Kuhn National Security Institute Virginia Tech Arlington VA USA Sanghani Center for Artificial Intelligence & Data Analytics Virginia Tech Arlington VA USA Information Technology Lab National Institute of Standards and Technology MD USA

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

artificial intelligence (AI) models use statistical learning over data to solve complex problems for which straightforward rules or algorithms may be difficult or impossible to design; however, a side effect is that models that are complex enough to sufficiently represent the function may be uninterpretable. Combinatorial testing, a black-box approach arising from software testing, has been applied to test AI models. A key differentiator between traditional software and AI is that many traditional software faults are deterministic, requiring a failureinducing combination of inputs to appear only once in the test set for it to be discovered. On the other hand, AI models learn statistically by reinforcing weights through repeated appearances in the training dataset, and the frequency of input combinations plays a significant role in influencing the model’s behavior. Thus, a single occurrence of a combination of feature values may not be sufficient to influence the model’s behavior. Consequently, measures like simple combinatorial coverage that are applicable to software testing do not capture the frequency with which interactions are covered in the AI model’s input space. This work develops methods to characterize the data frequency coverage of feature interactions in training datasets and analyze the impact of imbalance, or skew, in the combinatorial frequency coverage of the training data on model performance. We demonstrate our methods with experiments on an open-source dataset using several classical machine learning algorithms. This pilot study makes three observations: performance may increase or decrease with data skew, feature importance methods do not predict skew impact, and adding more data may not mitigate skew effects.

关键词： Training Hands Machine learning algorithms Statistical learning Software algorithms Training data data models Frequency measurement Software measurement artificial intelligence

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Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers

arXiv

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arXiv 2025年

作者： Lukas, Lukas Mueller, Jonas Leo Loeffler, Christoffer Schwinn, Leo Eskofier, Bjoern Zanca, Dario Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen Germany School of Informatics Pontificia Universidad Católica de Valparaíso Valparaiso Chile Data Analytics and Machine Learning Lab Technische Universität München Munich Germany Institute of AI for Health Helmholtz Zentrum München Munich Germany

Understanding the perceptual invariances of artificial neural networks is essential for improving explainability and aligning models with human vision. Metamers—stimuli that are physically distinct yet produce identical neural activations—serve as a valuable tool for investigating these invariances. We introduce a novel approach to metamer generation by leveraging ensembles of artificial neural networks, capturing shared representational subspaces across diverse architectures, including convolutional neural networks and vision transformers. To characterize the properties of the generated metamers, we employ a suite of image-based metrics that assess factors such as semantic fidelity and naturalness. Our findings show that convolutional neural networks generate more recognizable and human-like metamers, while vision transformers produce realistic but less transferable metamers, highlighting the impact of architectural biases on representational invariances. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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